{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Spinal Cord Gray Matter Segmentation Using PyTorch.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/omarsar/mri-analysis-pytorch/blob/master/Spinal_Cord_Gray_Matter_Segmentation_Using_PyTorch.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "6Ocm6A50JLtO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Spinal Cord Gray Matter Segmentation Using PyTorch\n",
        "In this notebook we are going to explore a medical imaging open-source library known as [MedicalTorch](https://github.com/perone/medicaltorch), which was built on top of PyTorch. The purpose of this tutorial is to show you the basic functionalities of the tools offered in MedicalTroch, such as pre-processing of images, transformations, and data loaders. With the tools you will be able to explore and preprocess MRI-based datasets that contain images such as shown in the image below. As you progress in the tutorial you shall begin to appreciate the capabilities and possibilities of the medicaltorch library for medical imaging AI applications. \n",
        "\n",
        "![alt txt](https://docs.google.com/drawings/d/e/2PACX-1vQmKTAQEz_zoomKa0HchswOUunHQTO3gDoH_VnXfcZdl8N3H0L-aGvM9GqQiiLi-bL0ME4-IoU6cv7g/pub?w=928&h=499)\n",
        "\n",
        "Image credit: [Perone et al.,2017](https://arxiv.org/pdf/1710.01269.pdf)"
      ]
    },
    {
      "metadata": {
        "id": "r8sWyzGOJ02-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Installing Libraries\n",
        "Below we will show you how to install the medicaltorch library along with some other libraries."
      ]
    },
    {
      "metadata": {
        "id": "bzzQn1BQpHHu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "2a7b2fba-baa2-4fe7-c1cf-671c397f29a8"
      },
      "cell_type": "code",
      "source": [
        "!pip3 install http://download.pytorch.org/whl/cu80/torch-0.4.0-cp36-cp36m-linux_x86_64.whl \n",
        "!pip3 install torchvision\n",
        "!pip install medicaltorch\n",
        "!pip3 install numpy==1.14.1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch==0.4.0 from http://download.pytorch.org/whl/cu80/torch-0.4.0-cp36-cp36m-linux_x86_64.whl in /usr/local/lib/python3.6/dist-packages (0.4.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchvision) (0.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.1)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.3.0)\n",
            "Requirement already satisfied: medicaltorch in /usr/local/lib/python3.6/dist-packages (0.1)\n",
            "Requirement already satisfied: torchvision>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from medicaltorch) (0.2.1)\n",
            "Requirement already satisfied: tqdm>=4.23.0 in /usr/local/lib/python3.6/dist-packages (from medicaltorch) (4.26.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from medicaltorch) (1.1.0)\n",
            "Requirement already satisfied: torch>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from medicaltorch) (0.4.0)\n",
            "Requirement already satisfied: nibabel>=2.2.1 in /usr/local/lib/python3.6/dist-packages (from medicaltorch) (2.3.0)\n",
            "Requirement already satisfied: numpy>=1.14.1 in /usr/local/lib/python3.6/dist-packages (from medicaltorch) (1.14.1)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.2.1->medicaltorch) (5.3.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.2.1->medicaltorch) (1.11.0)\n",
            "Requirement already satisfied: numpy==1.14.1 in /usr/local/lib/python3.6/dist-packages (1.14.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CEu5fBCvpMqi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AkvGLISDp0Ci",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "91c2cd8a-15f4-4b86-bfd9-531f1d1d4321"
      },
      "cell_type": "code",
      "source": [
        "print(torch.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Tel5eU7DDF39",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3db6772e-fa44-45d3-d762-c7be257c7749"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "print(np.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bh8sG3huKAK_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Mounting Data From Google Drive\n",
        "I have stored the MRI images on my personal Google Drive, but you may request the data from the [GM SC Challenge](http://niftyweb.cs.ucl.ac.uk/challenge/index.php#citation) website."
      ]
    },
    {
      "metadata": {
        "id": "0dEjMdM0sKJW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f083871d-80d9-4e06-cbc6-e84051986b85"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qy4iVLD2K0QO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries and Let's Get Started!\n",
        "Let's import the necessary libraries includinf the utility functions from the `medicaltorch` library."
      ]
    },
    {
      "metadata": {
        "id": "yD3mbIc8tzed",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "import time\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "from medicaltorch import datasets as mt_datasets\n",
        "from medicaltorch import models as mt_models\n",
        "from medicaltorch import transforms as mt_transforms\n",
        "from medicaltorch import losses as mt_losses\n",
        "from medicaltorch import metrics as mt_metrics\n",
        "from medicaltorch import filters as mt_filters\n",
        "\n",
        "import torch\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torch import autograd, optim\n",
        "import torch.backends.cudnn as cudnn\n",
        "import torch.nn as nn\n",
        "\n",
        "import torchvision.utils as vutils\n",
        "\n",
        "cudnn.benchmark = True\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1z836WKgLCXC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Data Exploration\n",
        "Before we do any modeling stuff, let's investigate our data first. Let's look at one sample (MRI image) from the dataset. We will see the preprocessing module `mt_datasets.SegmentationPair2D` which is used to read and format the data in a way that we can better explore it in our environment. See the example below."
      ]
    },
    {
      "metadata": {
        "id": "bGxyNTVItVYK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ROOT_DIR_GMCHALLENGE = \"/gdrive/My Drive/DAIR RESOURCES/PyTorch/medical_imaging/train/\"\n",
        "mri_input_filename = os.path.join(ROOT_DIR_GMCHALLENGE,\n",
        "                                          'site1-sc01-image.nii.gz')\n",
        "mri_gt_filename = os.path.join(ROOT_DIR_GMCHALLENGE,\n",
        "                                       'site1-sc01-mask-r1.nii.gz')\n",
        "\n",
        "pair = mt_datasets.SegmentationPair2D(mri_input_filename, mri_gt_filename)\n",
        "slice_pair = pair.get_pair_slice(0)\n",
        "input_slice = slice_pair[\"input\"]\n",
        "gt_slice = slice_pair[\"gt\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "40n8eVQkwlCG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "62aa9cb8-9783-49e1-d05a-b7a9fb403c4f"
      },
      "cell_type": "code",
      "source": [
        "print(input_slice.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U5zCh2ueML0D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "As you can see above, images are 100 X 100 dimensions. We can also view the image in our environment using matplotlib."
      ]
    },
    {
      "metadata": {
        "id": "BF7Teg-EtuEm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "b1552aed-c7f3-424c-8f7e-dc64f53e01f7"
      },
      "cell_type": "code",
      "source": [
        "img = input_slice\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e+BqGlei/BeDQwv8fB/DJUsrNAHYCeN+QYyQSiUXCQHHWNM3lAK4E8KWFt94C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\nkD/0RGIMkD/0RGIMkD/0RGIM8P8BbxRgYr3+dcUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5077e739b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JTMQ2HN4y-p7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "eb401474-2e51-4f04-b448-12a5a3d0f8f0"
      },
      "cell_type": "code",
      "source": [
        "img = gt_slice\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD7CAYAAABDsImYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEOXCjB0qQBDlwowdKmA/wKtRvcZL+4+eQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5073afcda0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "e-pnIwP2MXtC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The upper image shows an *in vivo axial-slice* sample while the lower image shows the spinal cord gray matter segmentation. Intuitively, the dataset contains the necessary information to conduct gray matter segmentation using deep learning techniques such as encode/decoder frameworks and deep convolutional neural networks."
      ]
    },
    {
      "metadata": {
        "id": "IUFHeSBwzgZ7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## PyTorch Data Loaders\n",
        "Let's try to use the data loaders offered in medicaltorch to test the transformation functions. PyTorch offers a \"transforms\" module that helps us to apply any transformations on our data that we may wish, this is by stacking operations a seen below. First we resample the dataset so that all samples are of the same size and then apply a cropping area, followed by a tensor type transformation."
      ]
    },
    {
      "metadata": {
        "id": "1wUOf_CMzg6Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# transformer\n",
        "composed_transform = transforms.Compose([\n",
        "            mt_transforms.Resample(0.25, 0.25),\n",
        "            mt_transforms.CenterCrop2D((200, 200)),\n",
        "            mt_transforms.ToTensor(),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qaufm7yizjca",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# load data\n",
        "train_dataset = mt_datasets.SCGMChallenge2DTrain(root_dir=ROOT_DIR_GMCHALLENGE, transform=composed_transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-c1ZteF4zlPh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a173d243-56dc-4287-be72-594c6a411844"
      },
      "cell_type": "code",
      "source": [
        "print(len(train_dataset))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2204\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "F7aY-hczOxuN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "You can see an example below of how a raw image was converted to a tensor format."
      ]
    },
    {
      "metadata": {
        "id": "Q89Awl_9z3yl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "01af72fc-e253-4df3-a43a-160b112c9b6c"
      },
      "cell_type": "code",
      "source": [
        "# sample of the training dataset\n",
        "train_dataset[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gt': tensor([[[ 0.,  0.,  0.,  ...,  0.,  0.,  0.],\n",
              "          [ 0.,  0.,  0.,  ...,  0.,  0.,  0.],\n",
              "          [ 0.,  0.,  0.,  ...,  0.,  0.,  0.],\n",
              "          ...,\n",
              "          [ 0.,  0.,  0.,  ...,  0.,  0.,  0.],\n",
              "          [ 0.,  0.,  0.,  ...,  0.,  0.,  0.],\n",
              "          [ 0.,  0.,  0.,  ...,  0.,  0.,  0.]]]),\n",
              " 'gt_metadata': <medicaltorch.datasets.SampleMetadata at 0x7f5071212fd0>,\n",
              " 'input': tensor([[[  263.9700,   243.9216,   203.8249,  ...,  1864.4967,\n",
              "            1690.7444,  1603.8682],\n",
              "          [  245.5923,   226.3793,   187.9533,  ...,  1761.1224,\n",
              "            1576.0929,  1483.5781],\n",
              "          [  208.8370,   191.2947,   156.2101,  ...,  1554.3738,\n",
              "            1346.7898,  1242.9978],\n",
              "          ...,\n",
              "          [  654.0775,   646.1417,   630.2701,  ...,   741.7890,\n",
              "             706.7045,   689.1621],\n",
              "          [  652.4068,   645.3063,   631.1055,  ...,  1040.8435,\n",
              "             958.9795,   918.0474],\n",
              "          [  651.5715,   644.8887,   631.5231,  ...,  1190.3708,\n",
              "            1085.1171,  1032.4901]]]),\n",
              " 'input_metadata': <medicaltorch.datasets.SampleMetadata at 0x7f50711c7e80>}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "-ySWB4PpPGFE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now that we have the image loaded and transformed into tensors, we provided it to the PyTorch native Dataloader function to do its magic. The DataLoader below basically creates mini-batches and shuffles them."
      ]
    },
    {
      "metadata": {
        "id": "8qeVpO6kz5s1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# PyTorch data loader\n",
        "dataloader = DataLoader(train_dataset, batch_size=4,\n",
        "                        shuffle=True, num_workers=4,\n",
        "                        collate_fn=mt_datasets.mt_collate)\n",
        "minibatch = next(iter(dataloader))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1-IZvi2qz7jG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c4d55f70-7f08-4459-d252-9eabb66256b2"
      },
      "cell_type": "code",
      "source": [
        "# check minibatch size\n",
        "minibatch['input'].size()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 1, 200, 200])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "fy9R5Ju2Pl3B",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Visualizing Batches\n",
        "Below we show you how to visualize batches."
      ]
    },
    {
      "metadata": {
        "id": "8O0TyYdmz97G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1025
        },
        "outputId": "93f8b922-81b5-4447-b6fd-33b824d38388"
      },
      "cell_type": "code",
      "source": [
        "for item in minibatch['input']:\n",
        "    plt.imshow(item.squeeze(0))\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qfibwzTff1Ne+9jUdHBykIF21WtX169fT/0j0ubk51Wo1bW5uFtB1salChF+2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\nJv4wmUwKUpt7zrXUci0DeeMGpzKN/zgUKwQdMvxeqFqtpnvDqlldXU3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QlAgyxtHRUUrB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\nd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Uk/bu/utx+3+3pYOS+3/2OS/osklWX5ZUmzRVFMndO1vFfyUUmfvf/3b6k7kB4XOZT0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\nD2QgT6gMlH8gA3lC5f8DMjPhO9dydEwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f50710814a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ruBeRpEKhkJSLzxfPQhktFotUkIVcDiI+uIfPXPifVXON7+yutKpPxyJ2VpcB\nKBQKaV/1yclJWvhx55YvQF9IWHd8db4Qek9wkVZxcrQ5Fpb7ksgyHo/14MEDPX78OJUfYzHs7Oyo\nUqmo0WikhYVwkqzB9ShAD39xeKZ0GRqkxJkzxM5nkKHHePii9LAq30Ee/B/L6mPIfLh1ZnywmGSa\nMTY+tz7/nkzlkQFPuKJ5OJY8jpjpyf+ZY0cLjtRQZo4Keee9vb1MLQLyLEAYvhbpO892HoLxIM07\nvru0qofgyNRRHlEG3+BGzge5HdPpVP1+P92XftxUtlt6Qer2e3INCxFt6jBdWhW8KBQud5whCIeH\nh4kkcejD4keZQJYRJkMIiVE7JPOQ1fn5ecof8EQL3/0lraC4dGmZC4Vs3rVDSNCEx8k9XOaJHygc\nDtuQpKOjo5R7zuJiYUNGgiakVXw8bm5CMEE8rhQ8FOdpwjSH7Xlwm+/++Wj9PQnHswkjcovx+qjQ\n4xpy9yM+L+9zzEGz2UxFU1AaTha7svB8CScSuT9zGa23uzgIP+toNptlFLIjChQR4wTB5+nfhIbj\nLsfYXogyXryMn9mHtnbfdGNjI3MM93g81qNHjyRdWn74AGk1oZubm2o2mymlFr+Nuua+YQXoPh6P\ndXR0lEkNHg6Heuedd1Jc1TfQ+Om0bnlwHfCxJWWscYSjcZH4dk92EQLzJSVeA7fD8w4o87S7u5tg\nPM8g/kwJsPPzc21sbGhvby8pQ+bFBde3Eset1dJqS60nZTly84pBkjKoAhfB2f4o9CR2uZL26/0e\n3B+E4oIbx52/S0ocDC4SSTmeRYk1li4jJl5tmOei3KnL7wovL2+BsWNdu8/vGZZUuuazxP/x+Snj\nneeexPZCnNXHC2HRpVXqL34QPpe/bLfbVbfblaRU4NAJGhJvdnd3M6WvJWVglOe7j0Yj9Xo9PXny\nJBUOlZQs7nw+T0U3SLBhoRK2QwFRY41sxQg7+YzvNATusgkHH5r+OfHDvdzNQRlR8ps+SquNUVL2\nwAwWNFWCo+WkbzHxJEJ1+BOu92y6vCw7Pht9dLfWkfzji8/6M6LlpblQxi8+5yFdSmE1m830nljq\nmIQDuQx5y/zD6TDH/j6ObpyUZoMW7xP7j1LnOlf2jkTIHLzJ35ees/DTYayjpAw0dPhbq9VSYYrB\nYKBut5u0rrQavGq1qmazqZ2dnWR5t7e3U64z98d/hpGfTqfpvsPhMBF1DnVns1naKgsC8W2T7vNK\nSoUW3N9l8bKISD9mPNxHRPmwWQRFwK47lCDFRNbWLk8+gq/A+nh0YLlcpmPPIJa2t7fTfgD3E1FW\neTDdobdbJhdKh/p8jsVLixnUvuKJAAAgAElEQVR40dr7ws5DA1HQnYuI7gF5BU4Cej6BdCn8k8lE\nGxsbeuWVV1KaOC4AawWC7fj4WMXiZcpvvV5PCJaxdELU3wGl4BugmPs8gjIS4Vh/OCVv9PHs7CyT\nlRnbC1HAE4vvGhXNxqYYiCjKa5Mg4xCVhc/mGdh07kcxC0mp9NLR0ZF6vV7a3caWV4f80sqq1mo1\n7e3taXd3N1l1fLiYhOMuDALvxNNkMknJPpISP4Afd3JyoouLi3RuIVbZeREsF1wI4UkvDELzsQWl\nrK+va29vL7NYfcMVC89dEhcwt74IWkQMtGiZ3Tr5Z3zMo2+fFzHw+1/3PT7X/48rJimNv3MuXn2J\nNeI+Nnssms1mck9x7SI5ys+8t//siCv+n6/owmBEULJORoIArmsvxKEdwFqP4WJFEeStra1krSHU\ngKqStLu7q52dnaQkgLi9Xk/j8Tjt1kLQ4sYfKVtWO1a1gTBsNpu6d++earVaBnI5QcVnWKwsHK+y\nwjuenp6m98G/Z1LZFELVVk4TchIStrjRaKjRaKher6fFh/D7AvBNN4QZQUdwB362Ae/hVZDcYklZ\nYg+r5daKsYnCHX1eh/vA7dj8PlEYXPnQ/Bon5WhOqkqXBgmYzoYwyFOyDafTaVLw+/v7ajabOjg4\nSPH6GD52gXRFEN021m2MYqB8vTiMuyySMqXB3ejEaIi35yr8kG9oLz+aGAgEfHWIxnnxm5ubmVN9\nvU4etdK63a6Oj4/V7/cTMy6twnaEBoGrvoHGw3DwDY1GIyV5OCR1i+eLD1cD5IEikpQ4BsKVoBlX\nKrhDLnBATpSDcxx5QuOLvVKpaH9/PykJjwaACNzHjIuMd6KiDdf5dykL0fOs+k1jlhfmi59zi8j1\neffKG4O80Bx9xQ1C4Tkr71uYcXVwAXHNuG80BDRfcyhyd0FcOdDXmATFenWF4mjNozR5Y0F7IWA/\nMXNYSkmZcBhaWFKKybNXnuvn87k6nU4ixUajUdofj1/vxAsDQzJHJFp8/zz9QeGgUX1RO2qg+X55\nnkH/XFGxi45KsOTz12o1NZtNNRoNVavVlNBBERRYd6yLb6+N5JiPOb4p7grjRXKQV/JhsTmC4G/O\n6l/3TGerGQdXFi6wLoQInDd/DuPp7iEKOZJlzrf435yb4N4ofozAxsaGOp2OSqXSlfFm/sjT93fl\neSgAFCxo1AlK1oqfoMz1KKfpdJoUgaNkdwkYN97DycW8dlu3/7bdtg9oe66W31lYL4ghKZXMimyl\n+17unw6HQx0dHSUfnhi2kzXXpVt63BQrRP60Z7vF0lKxmizWyq1ChHH87jkMHtYkll0oFFJN9nq9\nrrW1tRSG9PMDybnnjANPFY0ZgoVCIcWwGVcKflDjEHJRukQJnmfh8Xx/F59Ph6Xu/8cxz/vOWGLZ\nuV9kvnk2c0A/nGuJLc+ViCFI6WpKMOO3vb2t6XSaOCgn/MgJYF1Gsm65XKZ1RITFXQlfT4tFNt2Y\ntcm4+HrxcK3vf/Aw7E3u03MVfj/IAKjFwiM+zWR4zJRjqkajUfJ/8e+9YAX39MQIJgbISB9iZpxv\n6pBWqa7SKmNtPB6n60ulUib2yjNYSEDTjY2NFFkYj8cZgmZ7ezux+o1GQ/fu3UtJOvP5PG1TprFI\nUILcC2IUgcC9IjrBmJMQAhfiJKCkxHqzQBmrWq2W8g18SzLvjSIi1HldBiDN3QWH9nALfM5hvROO\nHhZ2f5yxieE//3t0CWieRMT4M66UWvM5ZoxxL+kT17nvjeL1vAG/zok6d209kw83jPXmiVyeofrC\nEn7O7OLH+9nzbklgzdm5Ri6/n1tHiSomA6H3/eSeXcYAMdFYdvoCyqCPfg98ZUcOng4srXw+JtBJ\nHnbieS4BMd6YnMMk4js6UvDFjbDMZpclpGGIuT954MPhMCWKkCWIovRNRCiXmLnIBhe+fExRqC4Q\njIUrT2/OE7hlj8IfBdX9a7+nKxnvo7+DIwpn+70vkRdgPDc2NhL6QgnCn5TL5VTnACTrCol+M/4Y\nNw+xevjUCU3kAeMWIxm+rvnfCxvqY/CBoXmWie2K1LB/8uSJOp1OyrOHLJvNZgk5sEGGUJdDYVc4\nKAV+dpIEax4b2p+fPSsrMs2etIHg0w8SjLD+XA/ZVyqV0juCXog0MKFkl+EioGQ8T8HZ5uVymZ7r\nkQ2ITdBO3GzjMFhSqvcXY9ZEayAOY967jwstumIIIsKG0HKtL3IPqfF7JPy4rxO0kTiLrkmMVMQ1\ny5xKSgefIPwkCWEYyCz1qI3vQSEpaLlcpoxOT8zCDXQU7GRfdFuY/xiGzWsvjPD7HmVp5Yt2Op20\nVffo6EiPHz/W0dFRsvpoy3K5nBJcsPZoWCyzL0IPi/mmCYQCX9phNgvTLT5/c+vnDC5/RyC9ei6L\nL04UqIJJJP5br9dTSI939lLm/E4qcfS18RepJ0B1GHdxYPjps2fvxbljEbqweWVZ31/PonUr632L\n/qlb3jzEEK2dK153BzwkyN/cKqJQfKw8yzSikfhsfy/vD99Bj5FjYqyYX74TSsyLdPj7OSJwRt+N\njCvWvPZC7OojYcZDep1OR4eHhzo+PtaTJ0/08OFDvfXWW3r77bfV7XaT74ObgAJhv3y5XNbu7m6G\nwPFJdk1M5h/pwSSceOgmDqQrGCCY78eWsnX7eQbvRyizWCymv7lV8I0dkhICcgIPZYmFKRQKKeUZ\nDsNJJSD+2dlZQj2eIxDf1QWHxVatVjMbdDgTkL76RhiE361xHknovAjPZlz9szflVbhiibsH/dnu\nRlzHQQCtfccmLmJUFjzbITh7OXz+vC+MM6iPPRX1ej0ZFhpo1IlQ51AYE/8bsvRCZ/h5Ao10ydgD\n47vdbqqienJyonfeeScJ/nA4TNAyblrBh67X62mgsf4RAjvzzsRiseJpJxG6ukUg+cM370grS7tY\nLBLEx/JDJPlusG63myCjV+xFKfT7/Uz0g2IcLDhXUA6JHZYDUb1ir/vSUSA8R2E+n6vZbKaDRYDk\nUaHiSuQVAPEWnxddg7wxj+OPUETYzs9+P94nWukoIB5J8ChChNj+eScsWXfX9cN3dbqCBO5HAc8b\nw0iiRlQS8yby2guR3osPSSFESYm8IykGP5uNNKPRSMViMd0D3wvr5laYRe6Wxctc0fi/+8u+tz1O\nBIQUmXZe9EHK5l6zmcYLhAK56cPx8XE6BAJyCd+Zwo2bm5uJ2SWv3PvkFhyXxt2QcrmsnZ2dZG2w\n1tzPdyXyPyc4pUslBVnlxUJ8rzn8i/MmcfxcaCL89y9vTsjFe/hz/HrWgKMRrnOB9et97Fyw/XPS\nipiMBCLzFoWUMcZYxfRylDifc7KWOXFiNxYJwZUFmd7UXgi2nwMM/BxzIDICirViEqKfHAfeNR+T\n6f6pT65PjBMthUIhE/aK5BecAMU0PMVTWh0pTRovLLjzC6VSKb3zxsZGQgeFQiHBdxAFqIBnkNLs\nMV2UFO/j5wbggxJSjPu9uQdji/VDAbBI2fwUffJYPMXH058R27tZtqig8wi9PNQS10R0YyJvw71Y\nH35/h/vXoZk8vz5PcWDlKZPmW8fzFCTj4RadvuS9O5/J4w68vd/jur5a0mck/T9P//RZSX9V0s/p\n8uSeR5K+td1uT3Nv8LRh5R8/fpx5QUkpFIYFrtfrCf4iHGhAaVW7jHx/GFQ0pKR0gIKktEkGgozw\niVvbtbW1DDphkD0M6WQaQkFjVx4kGMxuuVzOpKWiYHZ3dzWfz3V8fJxyGeK2T08NBpnQN889xyp7\nARMvVeZ+MhbHY9XSipDCunkUgGKTrvBAPWyKornfG6183gKFT3BrSotMfIxEOLqTVuXNMRgoAZCL\nFxNhDSKAHsKVVsYhvps/M3IWkTR0rmBrayv9j9wBz5vgfoy/G6yoXGg+DriX17XPx/L/r+12+2P8\n0mq1/rakv9lutz/TarX+iqRP6F1q97/zzjuSpLfffjsVoYj+FDAJH5jQlv9PWvm4vhh8scWEB6wk\nCx9t6jkBhP+k7BFR/M1r87m74Ke5+OR4wQfPy+Z+ED4kL7ED0EN4uAO05XKZNul4mLRcLqdsPZSL\n1yTMi6FHyEkfIfF8Vx9CkudTxnHmeyT7mBefK9wkxjL6/X7fQmHFdDvSi6gwWl23mvSJPjvJRx/9\nXf13GmsuQn9HUd5cQVDMky+39Hx3Q8GzHN3GcfWx/ZMK9X21pO94+vOvSPrLehfhf/PNNyVdWn52\nR/ESsMqSUiadh8cYBPf5JaWIAYLpiiJCOf7v5ZbzkjIkJS3q1oBjl7gXLgCkZa/XSwLvVpp+upXm\nHaggs1wu031qtVry9UAGjAvFPD784Q9nMhIh3arVarLojBHpz2T1eRaZRzj4Pp/PU3ETSYlviacM\ng55QrCCk66wUipLPwfG4com7FLk/AuwQPkJ2F2BvWFCPrvg8u+DmWVXPPaAfjOF1bolHUPw9cM0g\nCanEy9r3eL7ngziB6SFldzH8M3ntfR3X9RT2/xeS/j9Ju5J+UNLPt9vtO0///7qkn2u32//iTfe5\nPa7rtt22P5GW6/i/X8v/B7oU+F+U9EDS/xzudT3LYO0nf/In9SM/8iP60R/9UTWbTe3u7iYYx5Zc\n9uM/fvxYjx8/1unpaeboag7VIN6PJr1z544ePHigO3fupDRfsqW4HnKMDDr8fb48m2+5XGZO7nV4\nDiTm+n6/rx/4gR/QD/3QD6VUYTbUsIXYCz9iGcvlssbjsR4/fqyTkxN1Oh0tFouUBxHPnyeF+d69\ne/rCL/xCNRqN5Nd64o6z/xB3hOrYtEI6s6QMsUdZ6JOTE41GI33zN3+zfvVXfzW9i59KDGrCEmL9\n+VvMswcl4X7Eba3cwy0ZbhfvxLMcbnvI0Te50JzDACEWi0W99NJLOjk5yRQWcRfCczoiSZwX1vNn\n4TI5Gcc7eK4JaI48Fvgo0IYfq8ZnnfXHPfBIxkc/+tFc+Xu/h3a8I+kXnv76RqvVeizpz7Rarc12\nu30m6WVJD9/tPp6gw5n1vrAJo3lVGodbDgdLpVKm3NX+/r7u3Lmjvb29dD1n1kur0B0DhBC6MM7n\n81RoYzabpQM8hsNhImgQIBY3ykRa7U/AtSgWiyncRw7AcDhM1+/s7KQxgMDE33PI5+w9bs5wOExF\nRijS4WWkJGXgZEzrJbzozHGhUEiRitPT07SAa7Vaqqno1Y+jv8sYOuT1zSnsUvSCk/ip1HFwnxkS\nFmF2nzYm/Piz/X/uzztcj4QcjTGO2YqRWMuD5K4QGHeUsysv+oSgO0lHNAil6BvRUJiuXJz3ugny\nS++f7f+4pHvtdvuvt1qtu5Je0uVxXX9e0t97+v0fvdt9Xn311fSd/H4mm6O1qX7DMdEsFFJIYbI5\nHJGKubu7u9rb21Oz2UyWBaZdyqbSSqsJcv8RTSytiCBCM6APjxCwgD15yfcJoExI9GHvAhaXjEG2\n8vKeLCJXUNKKXXcW2jmGyFbD3se67r6ByC0zYUqSeliwlP4CLXhmoJNinpvPmPpOSsK5zDWfcVIy\nL7TGc/ju/jlKweeYZLBIwPkacMLPOQP3//lfHncUiUz/HvuO0MZnOA8QkQX8iJOikdT2/uSRgLG9\nX9j/y5L+q6dHdFck/TuSflvS3221Wn9J0j/Vezi668u+7MskSa1WK72kp7eyeGB/PdTW7Xa1tram\nnZ0dSUp571tbW6lcNzUBPFEikkHSqpY9NQB8IXhlVFwA2HS+EPaocamii8tByS7IMxaAn62+vb2t\nnZ2dVKK83+9fSQxyUo89DdQvROg9K8/3HUAqAh+dJY9WjBDlbDZL7gvvxWnFvtXZBZ4F6uPu5Bzj\n5QueBc6Y8xnQmofGeB9X2ihKDz0yPw6T47P5rKRUMcdzQDxkFwU7kox54UwnPT10R5aq7yYFydHI\n0/D9GqAEJ7IdrZ6dnWXSu69r7xf2DyR9Q86//twf5T6EoCiBJCkzSEB1/HH8S3a4SUpQHL+1Xq9r\nf39fBwcHyZW4rsiDh+gIrbEIEDQWPIIOK+txaA/zOKRj0YEiuD+xfywbi300GqlUKiUfH5ju+Q7R\nhyexxuP3vBt+rfutHnVw39YtXkQKpVIpVUbmvaLy4JmucFxg8kJeLFwsIdET3xm4WCwyZwfCWyD0\nrqjZFeccirt5XEc0wteDv4NbTQQrWlT/f7xPFH53K1wJRgVBqNV35UWOgLly7idPIb4XIv+5ZviR\n5JNXEIKa+xcXl+fD7+zspLPtOQAzEniNRiOdiY47QLKLx8mlFcxndyAhp+XysuoKJ+Z6LTWHm9QW\ncLTglk5a+cxeLWe5XF6xzjQvHw4J6TF2J4iklcvicNQXn+ccSCu3wjMUfeG6n+7PwZ1CCN3dYFwl\npSQVD8XmJfs4LMc6s/fBTxjGGjqhWCwWE1kZE6Di+HtiUqlUyiAo75uThCgXh99cCxr1RC7PGbgO\ngrvwu+JmDv3cBRLP6Ctjymf4PVYFdleQa5mf69pzFf63335bX/EVX6Fer5cyxVxTI7j4sFtbWzo4\nOEj73L1+eqVS0c7OjnZ3d9VoNFIcV1pNPDBWWjGsZ2dnVw7GhF9wuAjUco1N4QYWX4w3A2Hpp7sL\nDlnpp5/IU6lUtLu7q0JhtVMv3j9Pyzty4pq8JJdISvFztGqgCU6gkVZQlHH0slJYXoeizqV4bB1X\ni/Hh3Z3r4Cgx7oNr4gy557mTf8Aa4nOFwmpvvecmoAAdeeZFB9yyOtrJG0Mf+6hQo/vgn0cpQXaz\nJjwRKirsmCtyU7ZjbM9V+D/72c/qm77pm/S5z31OBwcHmfpxwFwUAuzywcFBEmL3D6XsomQRYF05\nIKPX60laHWjgNQHZjLK9vZ1OEXIIDDpw8gXt6paXiaPYCFbMNwe5APqWXuBfvV5P4cgnT54kH9Gh\nrPuLLFCv6eeRCCm7fTZmhzkU9YgLoTLnGlAIWEHPS8f9QFkC5RHUs7OzjPCjHEnZ5rO+BRolcnBw\nkAyBhy2dQykWi0lJAaPZOh19Y8YwCj9CyLhFcs6TouJ9vLlLGDMIPXOPZzqK5f6gIK5lIxiulUcy\nWPfOY8SNP96eq/DTPPQVfVaE02PICBiLS8pqVM+KgmjjXD8vfsmCRsg89IWgulXzSQCVsAhns8uT\nVYlQSNlqM0Bu+ulZb/7O+O++O85Dds6GR0GWsnFnFrwvSrcS0fq4Dy2tyqt5JMGv5ysiC+6FYBIa\nPT09zdS/cyTFfdjC7O6UhwY5lIXx8IpIwGfey6MsTkTiLyNEeT6yryf/nxOArF23yJFpj5mifp9o\n8fm7c02gRd8YFjkVVxyRWHxhYf+XfumXSpI+9KEPpdx5hztMsjPrUnbbpfuRCMfa2loi8Xq9nh49\nepRSZfHhOWjBhR6BA566z+gkCwJFWiYDzaKlT37YI4vSjwHDcjqLTkrubDZLRUphrN3XlVYx47jf\nnDHLi2t77oHnAEgrdMPfUFpxO7MrV194jD8WezAYpENPORbNcw0iJHU/25UB9/dU4GJxlWzlZKoj\nIxSpJ3RFBep7CHg3TzK6zg1wXoT59757foNzCn4NLT6DeZKUwr6np6dpo5d/NkaAWLu8/wsL+w8O\nDiQpbW30/PHpdJriyJQ3ormv7JPgvxOf7vV6KY4MKShJe3t7GTIPa4tmJYGH5uEfJ4AQekgY33TD\nASNMvId5EHxXFrDZCA5CBNyj0W9yGrwoSCTwHOp6+CcSVFhqBEhSJirgC87DgW7ZqLuA0hoMBjo8\nPNQ777yTjiXzzUQ8m7EAAiMgjsqky5qFHnVg3BFer9tAY4zIRkRZ8c5O3tE8OuLK3gU7JtB4OBm0\n4WRf3F2XZ5k99MiaYL44Rcgz/3ieKzxHH5Fwje320I7bdts+oO25Wn6H8VQ16XQ6ki79O8J91I93\nzejFMKWrlv/8/DzV+iPe32w20y5AP7Yb/939JeC1V6nx8Bg8xGKxSC4Ff+c+lBLjHXEtpGyhS9ra\n2prG47GePHmS4Se8QEbMauSATpCLM+xY0shkeyjQcx3Is+e4LoejeVEF7oEPf3JykqD9YDDQYDDQ\nyclJqlDk1pk2m80SSuAATKArlt9hbL1e18HBQYaxBx0yV1hHaZWNSNiW8fGy58ybdP15g275fRxY\nA6xPxsvRVnQbCoXsWQvuTnoOibRCFOwrwX0BLcUxRT4Y2xcW9nvW1Xg81tHRUdquCkS8d+9e8q2Z\nAK53/8chpMfG2bhSq9VUr9dTrBrfF3jGBDmRAh8gXS3jBYTGP/QFxER4bj6TCywlmhHj5L1eT/1+\nX/1+P7H7LGyy8zyrkcSnWDJLyha7oM/RP8dndkIp7k2QriZIOdPP/odOp6Ojo6NEwHEKMr4q24vh\nOObzeYqGULzFD1Il6sGzEVS4FJQg+yXW1tbSfhDu4UqQ92eNwB/EXHqaQ+j4u/MrTtbRV4+mxHCr\n358WiTl3jVh/7oIsl8srMsBzPc/hpvZchZ9FgOAfHh6mxB8WAWm8rpGn06l6vV4qdCmtEk+kFYtM\nCipHeTcajUwqrvvf7gPCDyB4kq4oCmlFFsU0X8/w84wyEI5XyCGmLV1aTk4TLhQKKc+A8FmtVtPe\n3l7iSjjYI4arICMjoom+Nlb3yZMnmk6n2tjYSFZSUobQjPdHYSDczNVwOFS3202W3zkL5oH7T6fT\nzF4HZ/ax9hcXF+r3+5KUENFyuVSr1dKrr76qg4MDHR8fJ5TnFZJns5kODw8TWvIwX2TpPVTmkRiU\ns1tzF36sdgwhIqDxABPWHQrJnxOz+riev/neDhRBp9O5UmIdA+MIKK+9EMd1sWsMRlNaFSIkTuwL\nlw03CB7XS9l67xTpIHbvhS0YbKCTwyPgs4dgWAQer4WkQvgR8IgAsC5MupN9vn8AZUJ/EX7Cj05e\nSUrKzfvvhKSTZ/7OLMrxeJyEd7lcptwCJ0KjwpMuEQo78gihSpe7Hfv9vnq9XgrpMUYoMbIteV8O\nZPGYNGNEvxmfXq+n+Xye5vHg4CBz1LjHxJkjtl2zMcmFPpJ5jB/Nx+s64fRwnbsFwO+8iIb/7O6L\nW2rvT4wOuPs7GAwyz/FIRUxhju25Cj8Qv9vtpngwE7e3t5cYeYQUbYnAeUgkbgcmRu0bUGJVGKAZ\ngxuhvVsE/CeEFevtjDALxaGfh8WkFcuLhfIkGeB7pVJJ0NbDeCg75znwY+mrWwWPu3O91x3AUhPS\n823AvD/fPYR0fHychJaz/phHvkAvkjJFRwlhSkoHshwfH2dCjIwXqMV3HS6XSz169Cj1+cGDB2lL\nOBta3Poydp53UCwW0+Gnbv39Wkc8rDmPLPj1ET2gAHxteHPXyw2Cz2NURi7EIDo4H9wuXyfF4mWN\nwBc2yceTYQjD8ZIvv/yyDg4OUpYewoY1ZdHHEAcDg6WJwh99KZ8YNKZ0NRRzk3XAkvtESqu4OVt3\n3RJ5sUbegXp7vmsOBQKZGZ/tSMetvpOTLlT4+Fjp2WyWBB+kERca4czxeKxms5n2NAyHQ/V6veSq\ncc4CShxBZMMNaMDLnHEGg4dB8VsRTg8rTqdTdTodlcuXhVzW19f1+uuvJ6XnYTj3j0EW1Ie4zsdm\nvKK/HFFA/GwMs3JNzPxzjiovBTs2NyZRGbOLVbq63wPkF0OM3l6Iuv37+/vpqC20/J07d7S2tpY5\ngJMJgQV2yOQZgZ6lh3BF3iAmYbjllJSQBpATq+OQ2heU+4T0CRKLRQ+R56cN7e3tpXGo1+uq1Wop\nMchrBErZSjn025Of3Gpct+DYzES9+LW1teQaeU4CDeHBh79//35KrR6Px+kYNUmp2o9HEPDrC4VV\nKXLfz09WoxOIxWIxRXk4E8HnmDE/OTnRzs6OXn75ZVWr1UwlZmkFxYlIOJkXcxXcZ/bIUiSB6Qc/\ne4SFMY4Zfg7buZdDdScjab426Vec43K5nArUuOvoe1puivM/V+En4WZ/f1/SpcD7oGKlBoNByttm\nsccXc3aesA5/81BIjA5Ere1+HRPF754xh4UC9mLpXOiwloPBIDHgQH0IPdfe+PTkogMFJWX2INDH\nmIMeBd6//G8sEFwjSnKhION9zs/PU3qutDoglFOV2C/hSVoxksC98Lu5BkFyhAXRyuYsFrYL9WKx\nULfb1ZMnT/TkyRPt7u6m/QHuCoIexuNxQoqkYYMkY5Qk8iRRoONYx/USUeV18xLdBb8m72dHMnwW\notl5JpLa8vrp7bkKP0J/9+7dpNXxf8kSOzw8VK/XU7FYvHJ0tWtg0jjZfkr81WOjEcbHmC7WiomM\nGt5DZ+6CeDZgVABe8hoib7FYpAxGctWlLGmEgJPn7SmcIIc8QWVcnGiKi9aFy2sL0ndXhCiwbreb\ncjCorYjwI9yeXemHsJDF6CSrtBJmBJyogJfw8t2Z+OuUOzs5OVGhcBn6G41GCcUQCkUwGO9isZg4\nFZQ5hsHRhXM40RhwX/fvCRm6UEek6Y0xdqTo6CIiN18Tnv3n6MLzFDAwGKjr2nMVfjqGoEqrHW7E\nvIfDYcrz51rfIOMcAKWlvPa+P0e6Kgiu4aPQxIFDYYA6vC9OEnniDlEHhM0PxlhfX8/Eq+Ex3HIU\ni8XM3gP6QZ/dEkgrssxZerf8fJ7wF+XT8nLOIQbZmEPIjfMTsfoeWiOKgeD7XvSbGgIEBMYYuGW+\nuLhI6K5cLie+4dGjRyoWi7p//35K/ZUuhZRci/F4nN7b5zNujHLhog8+bv7dx5RGWi1rK48jiHPo\n94qCH1tEc/4evDPIhv5c156r8EP8kOiBXywpFe2QViEtYu/+ogiTL2TXnq6NfQHGBI8IrzxTTlIG\n0uVpbFALC5d7kliU56pQo8BDa56bDhLA0rAoYnyYPtMnzzjzxUQ8HSYdlMTidwGQlFwu6ifA6pO5\nR76+Q32+nCn3d4hWKkZCfH965C+ia8Dc93o91Wo1ffjDH07HjnMd6wt+YWdn54q75FYdAwAK5D18\nreStF9/34fMR3cc4Jx3iKqAAACAASURBVE7axrXnz4mQPzbnCMhpcNSZ195vAc9vl/St9qevkPR/\nS9qSNHr6t3+/3W7/1vu5/227bbftj7+93xp+PyPpZySp1Wr9WUn/lqR/VtJfbLfb/+S93gfLz0GF\nZIdJl7FkknvwSWGAgamE86RsIY8Is/L8/LyUTP/doSD3cA4guglR03MvtHAk5rAksQCJoxWH7VhF\nb07+5TH8sY/uB+Ifk8XHPQiLSbqSouvx+U6no263mwmL8SwPMTnhikvjKby4CM5zSKsTkjwCA6qb\nTCYqFApp7gk7jkajhLaky6gQOxVjjP46XgTE5AjAuR7eM88VcCY+rhu/PhKMPp95BLSjsjwU5L8z\n5nl/j+1ZwP7/WNLHJf3Xf9QPAiM7nU7yLVlg4/FYy+UywfmdnZ0EUwlLeZilUFgVwnTfUcoevug8\ng/u4nvedx/67kDEZwFsUUYTN7CuA04gT5oLtf5dWKahOPiH8wN1CoZAhzYiEeEKNb3byXAkUJ2QX\nY0YYTVKC+/j8KGZqKU4mk1SkhDHkvX0vPQVJ4QNwE4DjnjmZp4y9BgMhvs3NTR0cHKTiH5PJRI8e\nPdLe3l4qCb+9vZ3Cq1I2Zs64INw+d7FGn39Fw+C8i/c/+vtRSfg7enJYFFpfa54w5Gva1xx9hnC8\nTtFIen/HddFardafkfTJdrv9ba1W63+R1JG0L+n3JH330wM8rm2j0WhJmOu23bbb9sfWchnEz9fy\n/9uS/s7Tn/9TSb/bbrffaLVaf0vSJyX99Zs+/Bu/8Rv62q/9Wv3Mz/xM2lUGxNvd3VWz2czkm8fQ\nmG8E8TAMaaFoSy9pTcuDZkBxYJ/niXs8moo9bEONBEyxWNSXf/mX67Of/Wyy0l6jjt+Bo55N5hs3\nrovnOyGISwRRSFah1xvEkpHUQ4TB0Q9fw+FQh4eHki5PUX7rrbdSOu/p6ak+/elP62u+5mtSQpVv\nlvI9DoQmHfJHSMyzjo+PE5LwXA1Pv2VsptOpjo+Ptbm5qQcPHmh9fT1lKlYqFb3++uv6uq/7OkmX\nWaLFYlH9fl9/8Ad/oG63mzZHsb4gAOfzuZrNpp48eZI2I5Ht6KRbRDlOtPJ3f0dQAqgU5OVW+6bI\nAH/3+0tKqDD20e/H80gii+3zFf6vlvTvSlK73f4H9vdfkfQX3u3DnIYDC10sroovNhoNNZvNTDUf\n36HmGWHSKoHEGWYgmsfmY4sT4H93/8wnwdN2eW4evHJWHmjnjH+hULiiYFgsHvIj2cf3unN/h6QI\nOgUxPVLB8xCuWPpruVxtmPJdeicnJzo5OUmbgKRLd43zE0ihllaJSK4EvI/O+EurbDrpcjGzY49r\nfSMO70v4DiUqrQ54IR+BPSM7OztqNBqqVqva2dlJ2X8Id0wS8zF1VyzP9aNFVyBPCPP8/OgmxP/l\nhRDzrr+O/X+3/0mfh/C3Wq37kobtdvu81WoVJP2Pkj7Wbre7ulQK70r8kRkGqUesXsrW2Iv+qW9Q\niVrZNbMvtjwSj2dHzZsXGsTiux/NAoqFQLzopwsZ/WZTCuXJ4oKJ5Jf7d5FMiwoGxcReb0//dYH1\nHXsscA/tMT8k9PR6vZTkM5lMEvkaSVfQhJN1vvPQrVi5XNbBwUFKZWYfgAtK9I1LpVIKnSLEKMWz\nszMNBoNMP3d3d9NWYhSY7x70ysN89wSx6NN7GDeuO8YxL17POsAIOQfk36OiieFcD3/eROrFNZTX\nPh/Lf0/SoSS12+1lq9X6tKRfb7VaI0nvSPpP3u0GED8syu3t7QRRWFwutB5D9s0b0io/2r8i+/pe\n+A3f8ukNwXLmmHuCWvK0u+cBuKAjyL6LLlp0h/dYQk9VZiFxuk2Ms6MouZ6TdmLaKQgE4cdydjqd\nRPpRoUdSqpHgewKYR9wPFIDvR4hRC96/Vqtpd3dXklKdBkdaroRBQIwXipTnnp+fJ+EfDAaaz+cp\np6Fer6et4D4+0TLnIQLmM08ZxMzI2CLE9/v5d/6ft07jva8Tfv+bR6fy2vsW/qcx/H/Nfv9FXR7Z\n/Z4bnW42m6kuvx/NVC6XE7Rnw0jM7XcIye/RR5ayFVel/Jxn8tHdD0djLxaLZE39syABMua8TyxG\n37MfmdjNzc3Mfm6H/J6e7FmFXpOAxewRAXgOYDP9xXXyMCLPheXvdDp69OiRpEtWn732hN6ky+3W\nzFe1Wk2uGv4nzwQRRSvmEQrpUqD39/eTInR30At+Mm4e/vKQHfAf4X/8+LHu379/ZQ+DKyHGy9dk\nFCYf2zyrHpl37hNRJc3DjW6h87IsIxcQ4X9EATfxB7E91ww/BB2/DEgqrcJYEGMOzyCUPH8aax+L\nGDiUzwu9OPxCCPwwEI9hY/W9MIeUzQKMv6N0oiA79PVtxL7bkHfi0E4UobsYHHyRRyK5MuRZjoQ8\nf53KuxTjkJR2I3LaEHMDlCbnwpGI39tzHxivmKuA4qQ67Xg8vpIlSEMBMv4xBo6i8XyE8Xicci1Y\nFx7miy2uER/TuLsvfs6/R6HPs87833kD5jWis7h+4zqm+ZjdZPWlF2RjD7vwvFAFyRke7/eyVn70\nkpQ9wALLmbd9khYhsqQrGtZ9QmC/p/m6LyYpCScRCOLlTCwWqFAoJN8WhCMp1aAj3ZkxYbFztBhC\nuFgsUoKUV/yhL5KSgNJPJ0J5Zz8jkOo73IOdiMvlMlUQajabGaXC+MX6BJGhRonE9Ft+RkDZ4ss9\n8/xr0FTcms04Satdhr7xxqMsQHY3Fi5s/rOjt0jG+hrgc84BxPTe6651tyKODc1dFndrI6q9yQ2h\nPVfh9xARzC+Wwc9vY/8+Ag/D7JPgrDcCz/18gnyh8hVJHPeZHRqDQKKPF58b75dHKkYST1otKixY\nXLCdTidtCOL+8CbuZ7MYXJjoJ/2RskU8x+NxytrzRCtCsK5Y4A3cUnmLCzISYZGz4D1BOfAPuHk+\nnp5x5xmR/n5c7+FOjAZj7MVR8oTEBcsJ4Ghxr/O789wAn6f4cxT2+Jw81Ppu0P7dhP+2bv9tu20f\n0PZcLb8zrJ6YQgMmV6vVlJgC3MR6xCQcoDyJOGh4mGWaW36H+o4WPCbP/SgK4RxD3IoLrI3HJbFz\nEasFweWuC3Fs3o/zBzh27OzsLEVEsMB8d2uL1fToQCS2sPrU7H/48KEePXqUCLNOp5Oq8FSr1Uwl\nY96f+3BPSZlTiCQl0s4Lo9IfrD/QeHt7O5MYA2kpKVXjIXTooV3un/eeEKDL5WXeP/Mbj2znuyMy\nR2sckhl5E4fxEe35/3yNeWONRCTDfdzldNeNdel9eC9wn/ZC1O2n475QveAEmX1eYNPhs5TdaEOs\nG39PylailZQgfPQXPXFHWvl0njm3ubl5xR/M4xb8WG0EhTJVroj8HcgFoB9nZ2c6PT1N5amd0MEP\nd8HygqEsVC/z5Z+F6PMQHwIvKW3X9cQd+huz9ZhHdyl4pgtqnuA4IQqvcHZ2dmUbtB9zfh2T7srI\nXT4UIfUe+D8cjZN6Pi9OKOfB8QjxfV26EvJ2HXzPIwX9ndwgRdY/JqP5c65rL0QNPz+NBO3tbL77\ndwiYC7K02gvuE0r9Nxagk1leZNLDc5BWhK/8OGmKSXilILgA75fX4cfqOEnHuxDKdOFH2HnW+fl5\n5sQb+A9JmYNMsYb+jn4op6QrCsvDqBTmGI1GmY0t9M3zEUBHKGPGzhWE8zcoD+bRLV1eTgOkYER5\nKM64wBE2xhIBhiB1BczBl/TRcwGYf7eqrlg9hBsFzPkG55joq8+x8wk+Ly7QtBjNQPHRHIXSUKYv\ntPB7/XngnNciiwk6vJTDdR90tof6ufCeJMICkbKnvLA4gc0+WX6IBIIMQ89WVCcNffJ5PkeGeUkq\n+jeZTDJFItgzwOm9pKSSqMI2XMaoUFgd0OGLiWIXMSmIPjrZ1+/300k71CP05vei7whsHgnmFt6/\nEHwP20YF5eHa9fX1BP0lZdBSbAgJa4N+gqCcbHSlRX6DR5k8WgFpel0uQCQ7fe0QTZCy0QC/9j1D\n9KdzHUnb6AbkRQuuved7evIfU8O6bm1t5bKjrl3dqjs0c4be94dj1Tx2HmO7ZIWRwOJRAha4CwKQ\n0QtsghRYJB7KqlQq6na7KTnI4/WufICh8fxAUAJHd+/u7ia/09/frUuhUEhuEsVAo4vhyGk6nerx\n48d68uRJyhnw+8OVEGaVlPbTx2xIFCnKHCXgCnU+n6eaf6wBrL2/M0eSj0ajdH+e78JMc3ToSUR+\nJp+jC0+Nns/nKbeh1+uleeA+rE2PXkThi1YW1ICbkmeZ+Tz39Hv5/UBILuAeXcqLNDgHdV17rsLv\nVt6TNqSsVsRauEDHVFqSVEhKcY0vrWCoQysXGP8O3I7CDwSNgx6tgv/MMxeLRdpwwx55cv8R/vF4\nnCw7ixhh8D0ObnXwa53b8EKmESb6mBJSOzw81MnJSaqX6CQYSMdrCLor5gvZjyHjekcGCLdnO/q8\n+3x53cOb4LaPtVtGXze+D4PxxtV0t411hAKEqAT1McaO1GIuQ967uP8ficA8vuAm4jDPNfDxiFzI\nTQrghSjgGZlX/ufCE7Oy0GxeA54kFfxWfDwqtkJuScoUmHRiDIaexc2E8ztJONLqFB/QAzvGSE/F\nwuGnTyYTHR0dpcQcj8FLl8UzxuNxOpySyjQgBpKAPOUYmL5cLtMpLixuFmlcAE5uwTF0u910TiDv\nXKlU0rZqdys4MgzY7OnJWPbJZJK4kbzNS9Jqq3JEB1gu8jlwE3CzPFOPcchr0Q1gC7SjmHhcG8iL\nNblYLFI14by8ANaTpx6zXhDAuK4RZncFHI25sMcNQz6G3N95F1cq7oLktecq/LyMH39Fcyvh1pMJ\nAGL7YQUgA8/eI4vOc/6lbF15t65xofqpQtVqNVk1YKAjBqwGfXLfli+upyJRrVZLi4k8ff8MSgVF\nwOKlAdULhUK613X7/x2egkQ4b+/09DSdacc1wH0OSGFheQ0BFy7CoSzUqLTjQnVW3KMC9BO/HwHD\nIkdmPn7eraSvHTgGFCVjAjT3OcdqFovFNJ/L5TIXTvMc+u0uSFzr7pNH6M73GCWIaMJdhJvc5byI\njLcX4qBOSjnFeCUCxf/Qom4h3IrMZrNEGlLXzf02Z/U9TwDfW1rBYRYsYa/5fJ72he/v7ycL4URk\nJId4B55RLpe1s7OTvqgpzzgA0UEqHD7p7kKv10uHnfhWWIg/5zdiaI1Gn0ejkY6OjpICQBFG8i0y\nyrgVKGC3wJCYRFJ4Hv/3754yix8OypFWHAKCCirhHV0xO7Hp0QosuLtQHv702Lm/g3M3bkhQTswx\nGYkeVUCAXcg9FBfngneK+zPimLnw8wxHBn4/jNlN7bkKPxod4XcShgXsRzi58MetmDDxLEiUAX6f\nC6l0Ncea/8cvID4KZ319PSkLrIB/3q0ILDzxZaDs+vp6KjLJ3gVJmSSSUqmUkMba2lqC0tSfl1bn\nHTAOWH3GKs8v5otTeDqdTiYH3uGlE1yenOO7DPmdhgKMSshTqR3xYZWJ/PA8lAF+P3PMQueL9+L/\nzgWBmAj5RSKSvseICLyOVyHi75Ftj0IdrbIbtNgijM9T1PE9XTnnIQtf4y804YdPzHZdD8vhB6K1\nHeJhLYFx0sqvhk0vFC5PckFgWPTuWjC5kWDBd/dcAvpFDgEbaeASsBjuk+Mr47t6AhNhrOFwmCwV\niUiw6UBuDtKEB8Dicew0QhJZeYeY/I7ipBwWcN/9diw5uQ302U9KcveK5kJC852ZjF+0/PAza2tr\nKdQG5HeBixl9nrzE/30u2Q9xfHysO3fuJEXqLhFz4TUPqtVq5v+++Yrm69H75q7HdcSbQ36e4e4M\nc8VYR1chKoq4ft11ugn23+b237bb9gFtLwTs99xtbw6lnehzuB/9o+VyeSXZx08u4XO4CJzu6loS\naOVwDc17cXGhfr+f0cBYQAgpLD9QH6vpZBEHWB4fHydegffiM3GLLEeYM04eznSCjb+7b8t4cs14\nPE6nIpHkkrfPHUvmmXagL97b0RrPZFzcAjrxJq32x+fBXU9jdaTg93TSzi2e++okMA0Gg1Qdyt09\nPsdnSGbCcuKq5YXy/B6+TfkmEo53ywvnRWsfm6/J+Iybws3Xtecq/DRCWH70k7PAzv4SP4aBpl1c\nXGgwGGSOxEYIuB+ZcjwTBRAngWs9NIhgUCbKIT4KBtKGZ/iCwecl7bjf7+v09FQnJyfpGUQmdnZ2\nVKlUUkiQaAXwE2XhexfOz8/V6/VSxILPxIWKKzUej9N+AcYKpUIGIWcmxCw89+ldmB3GSivWHOGJ\nbD2N/RvAfdwf+B76xfrwgq68u/cbF2O5vKx0dHp6qtPTU+3s7KSiIZHnceF3Us9TalEUKD5fp4w1\nvEaE9jGKE/MfuMZZf67n2ut4AVcKfq/rlEga92v/Y63Vav0pSb8k6W+02+3/vNVqfUjSz0kqSXok\n6Vvb7fa01Wp9XNJ3S1pI+vTTk32ubX5yi1tpSRlL7SSOp+L6PTjMgVpzkUVGqJk00ka9vDSWi0n0\nYpIx861QKCThw0LU6/Xkp0uXixElgWUdDoeZopjklnM9STyLxSJTQovQmpNa/Iy156TaYrGYKYhB\ncxJ1MpmkfjBe8CiEEuO+B/eDHXG4ckHR5ZGynr3I73HzTryXIxEUqNd29H65gEor/oL8Dz/Uhe+R\nRHNBixxDJPUYUyI99DuiAu4b7+PsPc2F3psjTb8ur2/+Hp+X8LdarS1J/5mkX7c//5Ckv9lutz/T\narX+iqRPtFqtv6vL03v+eUnnkn6z1Wr9g3a73bnu3iTDHB8fa7lcpuQUaTVxrqWd0UQoSMvkyGjf\n2AJk80Xmz57NZimVM6Z8YrVcMLE2sOxsHKLKEBWIPQxERVmsPuwz1XE8lIXiAPJDVFK9x3e8SSt3\nSVoRaaPRSFtbW7nJHW7pQDUcxeU75mjRjXAr5pWHHZaj7Bw2O3R3eFsorPYl+E5GxgU47kSp7/mI\n78h4eIYibkgUkCiUjqDotwscFp7P+5i6hXVF4n2ISiR+jhbhfEwa8mdeRxD6GF8XaZDem+WfSvrX\nJf0H9revlvQdT3/+FUl/WVJb0m+22+2eJLVarf9D0r/09P+5DcE9Pj5WoVBIByhIq8kAcrI4YMLZ\nEQcE5qw2398cQ3nRL8JvROlESAdTL2WhKcIJ1IPNJ53WLa5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HSo40aJGoi/93ix7/532N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\nRJ6FKY3OA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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5070c15e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jO2nI9VuTm6YYh2qN+Xe11qjH3zfuQKH9ttrchnnmeP2+LUD6uLy83Er5tVLz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\nzdEQcNkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f50711c2668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QaD4oo2tf856IwSLUpX2TM1u0Hblx1msu6Pz+yPnn7YwEg84zlqtoy7+r4hqPxylSAyBl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\n4/BZT7/1o7lJWq6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8fIzpdJoYm0yuYSAVTtpfHunQ3ykkWOcvfvGLGI1GeOtb34obN25g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\n+j+TIztzMvl/IAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5070bac320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pKo7LpP40KkA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Constructing The Segmentation Model\n",
        "We saw the images above, now we want to build the gray matter segmentation model with the MRI spinal cord images provided above. Let's define a helper function that helps to decide the final predictions of the model."
      ]
    },
    {
      "metadata": {
        "id": "jI-nlqfA0Mlh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def threshold_predictions(predictions, thr=0.999):\n",
        "    thresholded_preds = predictions[:]\n",
        "    low_values_indices = thresholded_preds < thr\n",
        "    thresholded_preds[low_values_indices] = 0\n",
        "    low_values_indices = thresholded_preds >= thr\n",
        "    thresholded_preds[low_values_indices] = 1\n",
        "    return thresholded_preds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tSnfGfkNbV2z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "And here are all the transformations to both the training and validation dataset."
      ]
    },
    {
      "metadata": {
        "id": "PzHLV9Rg0PUR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_transform = transforms.Compose([\n",
        "        mt_transforms.Resample(0.25, 0.25),\n",
        "        mt_transforms.CenterCrop2D((200, 200)),\n",
        "        mt_transforms.ElasticTransform(alpha_range=(28.0, 30.0),\n",
        "                                       sigma_range=(3.5, 4.0),\n",
        "                                       p=0.3),\n",
        "        mt_transforms.RandomAffine(degrees=4.6,\n",
        "                                   scale=(0.98, 1.02),\n",
        "                                   translate=(0.03, 0.03)),\n",
        "        mt_transforms.RandomTensorChannelShift((-0.10, 0.10)),\n",
        "        mt_transforms.ToTensor(),\n",
        "        mt_transforms.NormalizeInstance(),\n",
        "])\n",
        "\n",
        "val_transform = transforms.Compose([\n",
        "        mt_transforms.Resample(0.25, 0.25),\n",
        "        mt_transforms.CenterCrop2D((200, 200)),\n",
        "        mt_transforms.ToTensor(),\n",
        "        mt_transforms.NormalizeInstance(),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XiBw-mus0RSt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### training dataset with 80/20 split\n",
        "TRAIN_ROOT_DIR_GMCHALLENGE = \"/gdrive/My Drive/DAIR RESOURCES/PyTorch/medical_imaging/train/\"\n",
        "\n",
        "gmdataset_train = mt_datasets.SCGMChallenge2DTrain(root_dir=TRAIN_ROOT_DIR_GMCHALLENGE,\n",
        "                                                   subj_ids=range(1, 9),\n",
        "                                                   transform=train_transform,\n",
        "                                                   slice_filter_fn=mt_filters.SliceFilter())\n",
        "\n",
        "gmdataset_val = mt_datasets.SCGMChallenge2DTrain(root_dir=TRAIN_ROOT_DIR_GMCHALLENGE,\n",
        "                                                 subj_ids=range(9, 11),\n",
        "                                                 transform=val_transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c5dB05YU0S8S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "187b4ced-16f8-43ad-8eb2-b5856cfe73cb"
      },
      "cell_type": "code",
      "source": [
        "print(len(gmdataset_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1423\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iOJiyUKr0uOY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_loader = DataLoader(gmdataset_train, batch_size=16,\n",
        "                          shuffle=True, pin_memory=True,\n",
        "                          collate_fn=mt_datasets.mt_collate,\n",
        "                          num_workers=1)\n",
        "\n",
        "val_loader = DataLoader(gmdataset_val, batch_size=16,\n",
        "                        shuffle=True, pin_memory=True,\n",
        "                        collate_fn=mt_datasets.mt_collate,\n",
        "                        num_workers=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4xvvubINbeo4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model and Parameters\n",
        "Below we declare our model and parameters. Note that we are using GPU in this notebook. Also note that the model used below refers to the U-net convolutional-based architecture proposed by [Ronneberger et al., 2015](https://arxiv.org/abs/1505.04597), which essentially aggregates semantic information to perform the segmentation. See a figure of the U-net framework below. You can also refer to the medicaltorch [API documentation](https://medicaltorch.readthedocs.io/en/stable/modules.html#module-medicaltorch.models) for more available state-of-the-art implementations. \n",
        "\n",
        "![alt txt](https://docs.google.com/drawings/d/e/2PACX-1vT2miqwBsJpm9vX2lH7GRJaMWw3ym9Ld3MNY-10rpKIQJoXvfsRbIu1OpndIn4BJqYUtpq3wZcwmS9v/pub?w=921&h=624)\n",
        "\n",
        "Image Credit: [Ronneberger et al., 2015](https://arxiv.org/abs/1505.04597)"
      ]
    },
    {
      "metadata": {
        "id": "cwqL4S4i0v_8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = mt_models.Unet(drop_rate=0.4, bn_momentum=0.1)\n",
        "model.cuda()\n",
        "num_epochs = 10\n",
        "initial_lr = 0.001\n",
        "optimizer = optim.Adam(model.parameters(), lr=initial_lr)\n",
        "scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, num_epochs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lu2HXF7sbtm3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Some helper functions to produce the desired metrics for the model such as accuracy."
      ]
    },
    {
      "metadata": {
        "id": "u4gGMehXJybI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def numeric_score(prediction, groundtruth):\n",
        "    FP = np.float(np.sum((prediction == 1) & (groundtruth == 0)))\n",
        "    FN = np.float(np.sum((prediction == 0) & (groundtruth == 1)))\n",
        "    TP = np.float(np.sum((prediction == 1) & (groundtruth == 1)))\n",
        "    TN = np.float(np.sum((prediction == 0) & (groundtruth == 0)))\n",
        "    return FP, FN, TP, TN \n",
        "  \n",
        "def accuracy(prediction, groundtruth):\n",
        "    FP, FN, TP, TN = numeric_score(prediction, groundtruth)\n",
        "    N = FP + FN + TP + TN\n",
        "    accuracy = np.divide(TP + TN, N)\n",
        "    return accuracy * 100.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vJ8cOB2DcEWt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Training \n",
        "Now we finally train the model for spinal cord gray matter segmentation. We report the training and testing accuracy below and train for 10 epochs only. "
      ]
    },
    {
      "metadata": {
        "id": "psr9TSoI0x8d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "4c7124fb-448f-4599-8a19-71bb0a72804f"
      },
      "cell_type": "code",
      "source": [
        "for epoch in tqdm(range(1, num_epochs+1)):\n",
        "    start_time = time.time()\n",
        "\n",
        "    scheduler.step()\n",
        "\n",
        "    lr = scheduler.get_lr()[0]\n",
        "\n",
        "    model.train()\n",
        "    train_loss_total = 0.0\n",
        "    num_steps = 0\n",
        "    \n",
        "    ### Training\n",
        "    for i, batch in enumerate(train_loader):\n",
        "        input_samples, gt_samples = batch[\"input\"], batch[\"gt\"]\n",
        "\n",
        "        var_input = input_samples.cuda()\n",
        "        var_gt = gt_samples.cuda(async=True)\n",
        "\n",
        "        preds = model(var_input)\n",
        "\n",
        "        loss = mt_losses.dice_loss(preds, var_gt)\n",
        "        train_loss_total += loss.item()\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        num_steps += 1\n",
        "\n",
        "        if epoch % 5 == 0:\n",
        "            grid_img = vutils.make_grid(input_samples,\n",
        "                                        normalize=True,\n",
        "                                        scale_each=True)\n",
        "            \n",
        "\n",
        "            grid_img = vutils.make_grid(preds.data.cpu(),\n",
        "                                        normalize=True,\n",
        "                                        scale_each=True)\n",
        "            \n",
        "\n",
        "            grid_img = vutils.make_grid(gt_samples,\n",
        "                                        normalize=True,\n",
        "                                        scale_each=True)\n",
        "   \n",
        "    \n",
        "    train_loss_total_avg = train_loss_total / num_steps\n",
        "    model.eval()\n",
        "    val_loss_total = 0.0\n",
        "    num_steps = 0\n",
        "    train_acc  = accuracy(preds.cpu().detach().numpy(), \n",
        "                          var_gt.cpu().detach().numpy())\n",
        "    \n",
        "    metric_fns = [mt_metrics.dice_score,\n",
        "                  mt_metrics.hausdorff_score,\n",
        "                  mt_metrics.precision_score,\n",
        "                  mt_metrics.recall_score,\n",
        "                  mt_metrics.specificity_score,\n",
        "                  mt_metrics.intersection_over_union,\n",
        "                  mt_metrics.accuracy_score]\n",
        "\n",
        "    metric_mgr = mt_metrics.MetricManager(metric_fns)\n",
        "            \n",
        "    ### Validating\n",
        "    for i, batch in enumerate(val_loader):\n",
        "        input_samples, gt_samples = batch[\"input\"], batch[\"gt\"]\n",
        "\n",
        "        with torch.no_grad():\n",
        "            var_input = input_samples.cuda()\n",
        "            var_gt = gt_samples.cuda(async=True)\n",
        "\n",
        "            preds = model(var_input)\n",
        "            loss = mt_losses.dice_loss(preds, var_gt)\n",
        "            val_loss_total += loss.item()\n",
        "\n",
        "        # Metrics computation\n",
        "        gt_npy = gt_samples.numpy().astype(np.uint8)\n",
        "        gt_npy = gt_npy.squeeze(axis=1)\n",
        "\n",
        "        preds = preds.data.cpu().numpy()\n",
        "        preds = threshold_predictions(preds)\n",
        "        preds = preds.astype(np.uint8)\n",
        "        preds = preds.squeeze(axis=1)\n",
        "\n",
        "        metric_mgr(preds, gt_npy)\n",
        "\n",
        "        num_steps += 1\n",
        "        \n",
        "    metrics_dict = metric_mgr.get_results()\n",
        "    metric_mgr.reset()\n",
        "    val_loss_total_avg = val_loss_total / num_steps\n",
        "   \n",
        "    print('\\nTrain loss: {:.4f}, Training Accuracy: {:.4f} '.format(train_loss_total_avg, train_acc))\n",
        "    print('Val Loss: {:.4f}, Validation Accuracy: {:.4f} '.format(val_loss_total_avg, metrics_dict[\"accuracy_score\"]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/10 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
            "  \"See the documentation of nn.Upsample for details.\".format(mode))\n",
            "/usr/local/lib/python3.6/dist-packages/scipy/spatial/distance.py:1311: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  return float((ntf + nft) / np.array(2.0 * ntt + ntf + nft))\n",
            " 10%|█         | 1/10 [02:18<20:42, 138.06s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.4326, Training Accuracy: 83.7934 \n",
            "Val Loss: -0.8337, Validation Accuracy: 99.0838 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 20%|██        | 2/10 [04:00<16:59, 127.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.8463, Training Accuracy: 95.3780 \n",
            "Val Loss: -0.9135, Validation Accuracy: 99.5229 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 30%|███       | 3/10 [05:43<14:00, 120.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.8845, Training Accuracy: 92.9551 \n",
            "Val Loss: -0.9218, Validation Accuracy: 99.5451 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 40%|████      | 4/10 [07:26<11:29, 114.91s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9009, Training Accuracy: 94.8983 \n",
            "Val Loss: -0.9235, Validation Accuracy: 99.5632 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 5/10 [09:12<09:21, 112.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9107, Training Accuracy: 96.3178 \n",
            "Val Loss: -0.9240, Validation Accuracy: 99.5627 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 60%|██████    | 6/10 [10:55<07:17, 109.40s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9156, Training Accuracy: 95.7523 \n",
            "Val Loss: -0.9252, Validation Accuracy: 99.5817 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 70%|███████   | 7/10 [12:38<05:22, 107.44s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9206, Training Accuracy: 96.4490 \n",
            "Val Loss: -0.9251, Validation Accuracy: 99.5695 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 80%|████████  | 8/10 [14:21<03:32, 106.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9248, Training Accuracy: 96.6010 \n",
            "Val Loss: -0.9236, Validation Accuracy: 99.5680 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 90%|█████████ | 9/10 [16:03<01:45, 105.01s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9273, Training Accuracy: 96.8791 \n",
            "Val Loss: -0.9260, Validation Accuracy: 99.5777 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r100%|██████████| 10/10 [17:49<00:00, 105.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train loss: -0.9267, Training Accuracy: 97.0087 \n",
            "Val Loss: -0.9262, Validation Accuracy: 99.5775 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "pEmtOczpglkP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Final Words\n",
        "In summary, you learned how to process MRI image scans using a neat and powerful tool known as medicaltorch. In addition, you learned about how to preprocess, prepare and load the data using medicaltorch and PyTorch's build-in DataLoader module. Finally, you trained a model based on convolutional neural networks to conduct spinal cord gray matter segmentation. Feel free to explore more of the utility function provided in the medicaltorch API and explore different types of datasets. Until next time!"
      ]
    }
  ]
}